LDA-based data augmentation algorithm for acoustic scene classification

Deep neural network needs large amount of data for training, to obtain more data, many simple data augmentation algorithms have been proposed. In this paper, we propose a LDA-based data augmentation algorithm to extend the training set. The proposed LDA-based data augmentation algorithm uses the topic model LDA to detect the key audio words in the recordings, and further to detect the key audio events and non-key audio events for each recording; with the detected key-audio-event segments, for each acoustic scene class, the probability distribution of key-audio-event’s occurrence numbers, the probability distribution of key-audio-event’s locations under each occurrence number and the probability distribution of key-audio-event’s durations under each occurrence number is counted, and then the new recordings are generated according to these probability distributions. Experiments are done on the public TUT acoustic scenes 2016 dataset, and the experimental results show that compared with the other simple data augmentation algorithms, the proposed LDA-based data augmentation algorithm is more stable and effective, it can get better generalization ability for different kinds of neural network on different datasets.